Introductory Fisheries Analyses with R

Description

Introductory Fisheries Analyses with R Dedication Contents Preface Book Description and Intended Audience Companion Website Software Information and Conventions Acknowledgments Dedication Author Chapter 1 - (Very Brief) Introduction to R Basics 1.1 Why R for Fisheries Scientists? 1.2 Installing R and RStudio 1.3 Packages 1.4 Prompts, Expressions, and Comments 1.5 Objects 1.6 Functions 1.7 Data Storage 1.7.1 Vectors or Variables 1.7.2 Factors 1.7.3 Matrices 1.7.4 data.frames 1.8 More with Functions 1.8.1 Constructor and Extractor Functions 1.8.2 User-Defined Functions 1.9 Looping 1.9.1 For Loops 1.9.2 Apply "Loops" 1.10 Saving Results 1.11 Getting Help Notes Chapter 2 - Loading Data and Basic Manipulations 2.1 Loading Data into R 2.1.1 Working Directory 2.1.2 Database and Other Software Files 2.1.3 Text or ASCII Files 2.1.4 Internal Files 2.2 Basic Data Manipulations 2.2.1 Removing Individuals 2.2.2 Filtering or Subsetting Individuals 2.2.3 Selecting Variables 2.2.4 Renaming Variables 2.2.5 Creating New Variables 2.2.6 Creating a Length Categorization Variable 2.2.7 Recoding Variables 2.2.8 Sorting 2.2.9 Combined Manipulations with Piping 2.3 Joining Data.Frames 2.3.1 Combining Similar Data.Frames 2.3.2 Combining Data.Frames by a Key Relation 2.4 Rearranging Data.Frames 2.5 New Data.frame from Aggregation 2.5.1 Summarizing Catch per Unit Effort 2.6 Exporting Data.Frames to External Data Files 2.7 Further Considerations Notes Chapter 3 - Plotting Fundamentals 3.1 Scatterplots 3.1.1 Simple Scatterplot 3.1.2 Scatterplot with Different Symbols by Group 3.2 Line Plots 3.2.1 Basic Line Plots 3.2.2 Superimposing a Line Plot on a Scatterplot 3.3 Histograms 3.3.1 Single Histograms 3.3.2 Histograms for Multiple Groups 3.4 Bar Plots 3.4.1 Frequency of Individuals in Groups 3.4.2 Other Values 3.5 Fitted Model Plots 3.5.1 Line or Curve 3.5.2 Means Plot with Vertical Intervals 3.6 Some Finer Control of Plots 3.6.1 Plotting Parameter Options 3.6.2 Adding Text to a Plot 3.6.3 Simple Placement of Multiple Plots 3.7 Saving or Exporting Plots Notes Chapter 4 - Age Comparisons 4.1 Data Requirements 4.2 Age Bias Plot 4.3 Bias Metrics 4.4 Precision Metrics 4.5 Further Considerations Notes Chapter 5 - Age-Length Keys 5.1 Foundational Background 5.2 Constructing an Age-Length Key 5.2.1 Preparing the Aged Sample 5.2.2 Observed Age-Length Keys 5.2.3 Smoothed or Modeled Age-Length Keys 5.3 Visualizing an Age-Length Key 5.4 Apply an Age-Length Key 5.4.1 Classical Method for Age Distribution 5.4.2 Age Distribution with Standard Errors 5.4.3 Mean Length-at-Age 5.4.4 Assign Individual Ages and Summarize 5.5 Among Group Statistical Comparisons 5.6 Further Considerations Notes Chapter 6 - Size Structure 6.1 Data Requirements 6.2 Length Frequency 6.2.1 Tables 6.2.2 Histograms 6.2.3 Cumulative Frequencies 6.3 Proportional Size Distribution (PSD) 6.3.1 Gabelhouse Length Categories 6.3.2 Incremental PSD Indices 6.3.3 Traditional PSD Indices 6.3.4 Special PSD Indices 6.3.5 Confidence Intervals 6.3.6 Easy Calculations of Indices and Confidence Intervals 6.3.7 Tic-Tac-Toe Plots 6.4 Among Group Statistical Comparisons 6.4.1 Kolmogorov-Smirnov Test 6.4.2 Chi-Square Test 6.5 Further Considerations Conveniently Summarizing PSD for ALL Species Notes Chapter 7 - Weight-Length Relationships 7.1 Data Requirements 7.2 Weight-Length Model 7.3 Fitting Linear Regressions 7.3.1 Making Predictions 7.3.2 Visualizing the Fit 7.3.3 Assumption Checking 7.4 Among Group Statistical Comparisons 7.4.1 Factor and Dummy Variables 7.4.2 Models and Submodels 7.4.3 Model Fitting 7.4.4 Testing Predictors 7.4.5 Visualizing the Fit 7.5 Further Considerations Notes Chapter 8 - Condition 8.1 Data Requirements 8.2 Condition Metrics 8.2.1 Fulton's Condition Factor 8.2.2 Weight-Length Residuals 8.2.3 Relative Condition Factor 8.2.4 Relative Weight 8.3 Among Group Statistical Comparisons 8.3.1 Summary Statistics 8.3.2 One-Way ANOVA 8.3.2.1 Assumptions 8.3.3 Kruskal-Wallis Test 8.4 Further Considerations Notes Chapter 9 - Abundance from Capture-Recapture Data 9.1 Data Requirements 9.1.1 Capture History Format 9.1.2 Summarizing Capture History Data 9.2 Closed Population, Single Recapture 9.2.1 Single Group 9.2.2 Separate Groups 9.2.3 Assumptions 9.3 Closed Population, Multiple Recaptures 9.3.1 Schnabel and Schumacher-Eschmeyer Methods 9.3.2 Log-Linear Model Methods 9.3.3 Assumptions 9.4 Open Populations 9.4.1 Jolly-Seber Method 9.4.2 Log-Linear Model or Cormack-Jolly-Seber Method 9.4.3 Assumptions 9.5 Further Considerations Notes Chapter 10 - Abundance from Depletion Data 10.1 Leslie and DeLury Methods 10.1.1 Assumptions 10.2 K-Pass Removal Methods 10.2.1 Equal Catchability Among Passes 10.2.2 Catchability Differs in First Pass Notes Chapter 11 - Mortality Rates 11.1 Total Mortality Definitions 11.2 Total Mortality from Catch Curve Data 11.2.1 Data Requirements 11.2.2 Chapman-Robson Method 11.2.3 Regression Methods 11.2.3.1 Single Estimate 11.2.3.2 Comparing Estimates 11.2.4 Catch Curve Assumptions 11.3 Total Mortality from Capture-Recapture Data 11.3.1 Jolly-Seber Method 11.3.2 Log-Linear Model or Cormack-Jolly-Seber Method 11.4 Mortality Components 11.4.1 Estimates of M or F from Field Data 11.4.2 Meta-Analytic Estimates of M 11.5 Further Considerations Notes Chapter 12 - Individual Growth 12.1 Data Requirements 12.2 Growth Functions 12.3 Fitting Nonlinear Regressions 12.3.1 Starting Values 12.3.2 Model Fitting 12.3.3 Parameter Estimates 12.3.4 Predictions 12.3.5 Visualizing the Model Fit 12.3.6 Assumptions 12.4 Among Group Statistical Comparisons 12.4.1 Family of Models 12.4.2 Model Fitting Fit and Assess General Model Identify Any Differences Finding the Best Subset Model Model Selection with AIC or BIC 12.4.3 Summarizing the Model Fit 12.5 Typical Model Fitting Problems 12.6 Further Considerations Notes Chapter 13 - Recruitment 13.1 Stock-Recruitment Relationships 13.1.1 Data Requirements 13.1.2 Common Models 13.1.3 Fitting Models 13.1.3.1 Starting Values 13.1.3.2 Error Structure 13.1.3.3 Parameter Estimates and Confidence Intervals 13.1.3.4 Model Predictions 13.1.3.5 Assessing Parameter Significance 13.1.3.6 Visualizing the Model Fit 13.1.4 Additional Explanatory Variables 13.2 Spawning Potential Ratio 13.2.1 Single Values 13.2.2 Multiple Values 13.3 Year-Class Strength 13.3.1 Catch-Curve Residuals 13.3.2 Two-Way ANOVA Models 13.4 Further Considerations Notes References Subject Index R Functions (Demonstrated) Index R Functions (Mentioned) Index Scientific Names